Verification on the extreme scalability of STT-MRAM without loss of thermal stability below 15 nm MTJ cell

Kim, Ju Hyun; Lim, Woo Hyun; Pi, Ung Hwan; Lee, J. M.; Kim, W. K.; Kim, J. H.; Kim, K. W.; Park, Y. S.; Park, S. H.; Kang, Man-Sug; Kim, Y. H.; Kim, W. J.; Kim, S. Y.; Park, J. H.; Lee, S. C.; Lee, Y. J.; Yoon, Jae-Man; Oh, Sechang; Park, S. O.; Jeong, Seonghoon; Nam, Seok Woo; Kang, Ho-Kyu; Jung, Eui S.

doi:10.1109/vlsit.2014.6894366

Cited by 20 publications

(6 citation statements)

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“…At sub-40 nm dimensions, process damage can play a dramatic role on MTJ behavior, as was shown by Samsung [102] with MTJ dimensions between 15 and 50 nm. An improved process with reduced side-wall damage allowed the improvement of ∆ to 40 at 15 nm diameter.…”

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confidence: 77%

Magnetoresistive Random Access Memory

2016

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A review of the developments in MRAM technology over the past 20 years is presented. The various MRAM generations are described with a particular focus on Spin-Transfer-Torque MRAM (STT-MRAM) which is currently receiving the greatest attention. The working principles of these various MRAM generations, the status of their developments, and demonstrations of working circuits, including already commercialized MRAM products, are discussed.

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confidence: 77%

Magnetoresistive Random Access Memory

2016

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“…The current trend of STT-MRAM cell design is to switch from the inplane MTJ [ Figure 1(a)] to the perpendicular MTJ [ Figure 1(b)] to allow better scalability, longer retention, and lower Jc [9], [10]. The in-plane MTJ's scalability is limited by the aspect ratio (length/width in the lateral dimension) of the cell, as a sufficient shape anisotropy is required for thermal stability, while [11], [12].…”

Section: Stt-mram Cellsmentioning

confidence: 99%

Emerging Memory Technologies: Recent Trends and Prospects

Chen

2016

IEEE Solid-State Circuits Mag.

440

210

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Recent Trends and Prospectshis tutorial introduces the basics of emerging nonvolatile memory (NVM) technologies including spin-transfer-torque magnetic random access memory (STT-MRAM), phase-change random access memory (PCRAM), and resistive random access memory (RRAM). Emerging NVM cell characteristics are summarized, and device-level engineering trends are discussed. Emerging NVM array architectures are introduced, including the onetransistor-one-resistor (1T1R) array and the cross-point array with selectors. Design challenges such as scaling the write current and minimizing the sneak path current in cross-point array are analyzed. Recent progress on megabit-to gigabit-level prototype chip demonstrations is summarized. Finally, the prospective applications of emerging NVM are discussed, ranging from the last-level cache to the storage-class memory in the memory hierarchy. Topics of three-dimensional (3D) integration and radiation-hard NVM are discussed. Novel applications beyond the conventional memory applications are also surveyed, including physical unclonable function for hardware security, reconfigurable routing switch for field-programmable gate array (FPGA), logic-in-memory and nonvolatile cache/register/flip-flop

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“…This type of PMA is categorized as bulk PMA in contrast to interfacial PMA. In 2010, Ikeda et al found the interfacial PMA (i-PMA) in CoFeB/MgO-based MTJs [79]. It is very meaningful for real applications that they demonstrated a large TMR and a large PMA with a relatively low STT switching current, even though PMA has been observed at transition metal/ oxide interfaces before [80].…”

Section: Memristor Devicesmentioning

confidence: 99%

Memristor devices for neural networks

Jeong

Shi

2018

J. Phys. D: Appl. Phys.

113

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Neural network technologies have taken center stage owing to their powerful computing capability for supporting deep learning in artificial intelligence. However, conventional synaptic devices such as SRAM and DRAM are not satisfactory solutions for neural networks. Recently, several types of memristor devices have become popular alternatives because of their outstanding characteristics such as scalability, high performance, and non-volatility. To understand the characteristics of memristors, a comparison among memristors has been made, considering both maturity and performance. Magneto-resistance random access memory, phase-change random access memory, and resistive random access memory among the proposed memristors are good candidates as synaptic devices for weight storage and matrixvector multiplication required in artificial neural networks (ANNs). Moreover, these devices play key roles as synaptic devices in research for bio-plausible spiking neural networks (SNNs) because their distinctive switching properties are well matched for emulating synaptic and neuron functions of biological neural networks. In this paper we review motivation, advantage, technology, and applications of memristor devices for neural networks from practical approaches of ANNs to futuristic research of SNNs, considering the current status of memristor technology.

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Verification on the extreme scalability of STT-MRAM without loss of thermal stability below 15 nm MTJ cell

Cited by 20 publications

References 0 publications

Magnetoresistive Random Access Memory

Magnetoresistive Random Access Memory

Emerging Memory Technologies: Recent Trends and Prospects

Memristor devices for neural networks

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